Adaptive telemetry based on in-network cross domain intelligence

ABSTRACT

Disclosed are systems, methods, and computer-readable storage media for adaptive telemetry based on in-network cross domain intelligence. A telemetry server can receive at least a first telemetry data stream and a second telemetry data stream. The first telemetry data stream can provide data collected from a first data source and the second telemetry data stream can provide data collected from a second data source. The telemetry server can determine correlations between the first telemetry data stream and the second telemetry data stream that indicate redundancies between data included in the first telemetry data stream and the second telemetry data stream, and then adjust, based on the correlations between the first telemetry data stream and the second telemetry data stream, data collection of the second telemetry data stream to reduce redundant data included in the first telemetry data stream and the second telemetry data stream.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/215,098, entitled “ADAPTIVE TELEMETRY BASED ON IN-NETWORK CROSSDOMAIN INTELLIGENCE,” filed on Jul. 20, 2016, the contents of which isincorporated by reference in its entirety.

TECHNICAL FIELD

This disclosure relates in general to the field of computer networksand, more particularly, pertains to adaptive telemetry based onin-network cross domain intelligence.

BACKGROUND

Telemetry is the automatic measurement and transmission of data fromremote data sources. Sensors at the data source collect and forward datato a central server, where the data can be reconstructed, analyzed andstored. The data can be collected at various layers or domains,including application, platform (e.g., operations system) infrastructure(e.g., network) and physical (e.g., power/temperature). While thegathered data can be extremely useful in monitoring and managing anetwork, resources, etc., the amount of data collected, forwarded,analyzed and stored can be overwhelming. Accordingly, improvements areneeded.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited features andother advantages of the disclosure can be obtained, a more particulardescription of the principles briefly described above will be renderedby reference to specific embodiments thereof which are illustrated inthe appended drawings. Understanding that these drawings depict onlyexemplary embodiments of the disclosure and are not therefore to beconsidered to be limiting its scope, the principles herein are describedand explained with additional specificity and detail through the use ofthe accompanying drawings in which:

FIG. 1 illustrates an example network device according to some aspectsof the subject technology;

FIGS. 2A and 2B illustrate an example system embodiments according tosome aspects of the subject technology;

FIG. 3 illustrates a schematic block diagram of an example architecturefor a network fabric;

FIG. 4 illustrates an example overlay network;

FIG. 5 illustrates a system for illustrates a system for adaptivetelemetry based on in-network cross domain intelligence; and

FIG. 6 illustrates an example method of adaptive telemetry based onin-network cross domain intelligence.

DESCRIPTION OF EXAMPLE EMBODIMENTS

The detailed description set forth below is intended as a description ofvarious configurations of the subject technology and is not intended torepresent the only configurations in which the subject technology can bepracticed. The appended drawings are incorporated herein and constitutea part of the detailed description. The detailed description includesspecific details for the purpose of providing a more thoroughunderstanding of the subject technology. However, it will be clear andapparent that the subject technology is not limited to the specificdetails set forth herein and may be practiced without these details. Insome instances, structures and components are shown in block diagramform in order to avoid obscuring the concepts of the subject technology.

Overview:

Disclosed are systems, methods, and computer-readable storage media foradaptive telemetry based on in-network cross domain intelligence. Atelemetry server can receive at least a first telemetry data stream anda second telemetry data stream. The first telemetry data stream canprovide data collected from a first data source and the second telemetrydata stream can provide data collected from a second data source. Thetelemetry server can determine correlations between the first telemetrydata stream and the second telemetry data stream that indicateredundancies between data included in the first telemetry data streamand the second telemetry data stream, and then adjust, based on thecorrelations between the first telemetry data stream and the secondtelemetry data stream, data collection of the second telemetry datastream to reduce redundant data included in the first telemetry datastream and the second telemetry data stream.

DETAILED DESCRIPTION

Disclosed are systems and methods for adaptive telemetry based onin-network cross domain intelligence. A brief introductory descriptionof exemplary systems and networks, as illustrated in FIGS. 1 through 4,is disclosed herein, followed by a discussion of adaptive telemetrybased on in-network cross domain intelligence. The disclosure now turnsto FIG. 1.

A computer network is a geographically distributed collection of nodesinterconnected by communication links and segments for transporting databetween endpoints, such as personal computers and workstations. Manytypes of networks are available, with the types ranging from local areanetworks (LANs) and wide area networks (WANs) to overlay andsoftware-defined networks, such as virtual extensible local areanetworks (VXLANs).

LANs typically connect nodes over dedicated private communications linkslocated in the same general physical location, such as a building orcampus. WANs, on the other hand, typically connect geographicallydispersed nodes over long-distance communications links, such as commoncarrier telephone lines, optical lightpaths, synchronous opticalnetworks (SONET), or synchronous digital hierarchy (SDH) links. LANs andWANs can include layer 2 (L2) and/or layer 3 (L3) networks and devices.

The Internet is an example of a WAN that connects disparate networksthroughout the world, providing global communication between nodes onvarious networks. The nodes typically communicate over the network byexchanging discrete frames or packets of data according to predefinedprotocols, such as the Transmission Control Protocol/Internet Protocol(TCP/IP). In this context, a protocol can refer to a set of rulesdefining how the nodes interact with each other. Computer networks maybe further interconnected by an intermediate network node, such as arouter, to extend the effective “size” of each network.

Overlay networks generally allow virtual networks to be created andlayered over a physical network infrastructure. Overlay networkprotocols, such as Virtual Extensible LAN (VXLAN), NetworkVirtualization using Generic Routing Encapsulation (NVGRE), NetworkVirtualization Overlays (NVO3), and Stateless Transport Tunneling (STT),provide a traffic encapsulation scheme which allows network traffic tobe carried across L2 and L3 networks over a logical tunnel. Such logicaltunnels can be originated and terminated through virtual tunnel endpoints (VTEPs).

Moreover, overlay networks can include virtual segments, such as VXLANsegments in a VXLAN overlay network, which can include virtual L2 and/orL3 overlay networks over which virtual machines (VMs) communicate. Thevirtual segments can be identified through a virtual network identifier(VNI), such as a VXLAN network identifier, which can specificallyidentify an associated virtual segment or domain.

Network virtualization allows hardware and software resources to becombined in a virtual network. For example, network virtualization canallow multiple numbers of VMs to be attached to the physical network viarespective virtual LANs (VLANs). The VMs can be grouped according totheir respective VLAN, and can communicate with other VMs as well asother devices on the internal or external network.

Network segments, such as physical or virtual segments; networks;devices; ports; physical or logical links; and/or traffic in general canbe grouped into a bridge or flood domain. A bridge domain or flooddomain can represent a broadcast domain, such as an L2 broadcast domain.A bridge domain or flood domain can include a single subnet, but canalso include multiple subnets. Moreover, a bridge domain can beassociated with a bridge domain interface on a network device, such as aswitch. A bridge domain interface can be a logical interface whichsupports traffic between an L2 bridged network and an L3 routed network.In addition, a bridge domain interface can support internet protocol(IP) termination, VPN termination, address resolution handling, MACaddressing, etc. Both bridge domains and bridge domain interfaces can beidentified by a same index or identifier.

Furthermore, endpoint groups (EPGs) can be used in a network for mappingapplications to the network. In particular, EPGs can use a grouping ofapplication endpoints in a network to apply connectivity and policy tothe group of applications. EPGs can act as a container for buckets orcollections of applications, or application components, and tiers forimplementing forwarding and policy logic. EPGs also allow separation ofnetwork policy, security, and forwarding from addressing by insteadusing logical application boundaries.

Cloud computing can also be provided in one or more networks to providecomputing services using shared resources. Cloud computing can generallyinclude Internet-based computing in which computing resources aredynamically provisioned and allocated to client or user computers orother devices on-demand, from a collection of resources available viathe network (e.g., “the cloud”). Cloud computing resources, for example,can include any type of resource, such as computing, storage, andnetwork devices, virtual machines (VMs), etc. For instance, resourcesmay include service devices (firewalls, deep packet inspectors, trafficmonitors, load balancers, etc.), compute/processing devices (servers,CPU's, memory, brute force processing capability), storage devices(e.g., network attached storages, storage area network devices), etc. Inaddition, such resources may be used to support virtual networks,virtual machines (VM), databases, applications (Apps), etc.

Cloud computing resources may include a “private cloud,” a “publiccloud,” and/or a “hybrid cloud.” A “hybrid cloud” can be a cloudinfrastructure composed of two or more clouds that inter-operate orfederate through technology. In essence, a hybrid cloud is aninteraction between private and public clouds where a private cloudjoins a public cloud and utilizes public cloud resources in a secure andscalable manner. Cloud computing resources can also be provisioned viavirtual networks in an overlay network, such as a VXLAN.

FIG. 1 illustrates an exemplary network device 110 suitable forimplementing the present technology. Network device 110 includes amaster central processing unit (CPU) 162, interfaces 168, and a bus 115(e.g., a PCI bus). When acting under the control of appropriate softwareor firmware, the CPU 162 is responsible for executing packet management,error detection, and/or routing functions, such policy enforcement, forexample. The CPU 162 preferably accomplishes all these functions underthe control of software including an operating system and anyappropriate applications software. CPU 162 may include one or moreprocessors 163 such as a processor from the Motorola family ofmicroprocessors or the MIPS family of microprocessors. In an alternativeembodiment, processor 163 is specially designed hardware for controllingthe operations of router 110. In a specific embodiment, a memory 161(such as non-volatile RAM and/or ROM) also forms part of CPU 162.However, there are many different ways in which memory could be coupledto the system.

The interfaces 168 are typically provided as interface cards (sometimesreferred to as “line cards”). Generally, they control the sending andreceiving of data packets over the network and sometimes support otherperipherals used with the network device 110. Among the interfaces thatmay be provided are Ethernet interfaces, frame relay interfaces, cableinterfaces, DSL interfaces, token ring interfaces, and the like. Inaddition, various very high-speed interfaces may be provided such asfast token ring interfaces, wireless interfaces, Ethernet interfaces,Gigabit Ethernet interfaces, ATM interfaces, HSSI interfaces, POSinterfaces, FDDI interfaces and the like. Generally, these interfacesmay include ports appropriate for communication with the appropriatemedia. In some cases, they may also include an independent processorand, in some instances, volatile RAM. The independent processors maycontrol such communications intensive tasks as packet switching, mediacontrol, and management. By providing separate processors for thecommunications intensive tasks, these interfaces allow the mastermicroprocessor 162 to efficiently perform routing computations, networkdiagnostics, security functions, etc.

Although the system shown in FIG. 1 is one specific network device ofthe present technology, it is by no means the only network devicearchitecture on which the present technology can be implemented. Forexample, an architecture having a single processor that handlescommunications as well as routing computations, etc. is often used.Further, other types of interfaces and media could also be used with therouter.

Regardless of the network device's configuration, it may employ one ormore memories or memory modules (including memory 161) configured tostore program instructions for the general-purpose network operationsand mechanisms for roaming, route optimization and routing functionsdescribed herein. The program instructions may control the operation ofan operating system and/or one or more applications, for example. Thememory or memories may also be configured to store tables such asmobility binding, registration, and association tables, etc.

FIG. 2A, and FIG. 2B illustrate exemplary possible system embodiments.The more appropriate embodiment will be apparent to those of ordinaryskill in the art when practicing the present technology. Persons ofordinary skill in the art will also readily appreciate that other systemembodiments are possible.

FIG. 2A illustrates a conventional system bus computing systemarchitecture 200 wherein the components of the system are in electricalcommunication with each other using a bus 205. Exemplary system 200includes a processing unit (CPU or processor) 210 and a system bus 205that couples various system components including the system memory 215,such as read only memory (ROM) 220 and random access memory (RAM) 225,to the processor 210. The system 200 can include a cache of high-speedmemory connected directly with, in close proximity to, or integrated aspart of the processor 210. The system 200 can copy data from the memory215 and/or the storage device 230 to the cache 212 for quick access bythe processor 210. In this way, the cache can provide a performanceboost that avoids processor 210 delays while waiting for data. These andother modules can control or be configured to control the processor 210to perform various actions. Other system memory 215 may be available foruse as well. The memory 215 can include multiple different types ofmemory with different performance characteristics. The processor 210 caninclude any general purpose processor and a hardware module or softwaremodule, such as module 1 232, module 2 234, and module 3 236 stored instorage device 230, configured to control the processor 210 as well as aspecial-purpose processor where software instructions are incorporatedinto the actual processor design. The processor 210 may essentially be acompletely self-contained computing system, containing multiple cores orprocessors, a bus, memory controller, cache, etc. A multi-core processormay be symmetric or asymmetric.

To enable user interaction with the computing device 200, an inputdevice 245 can represent any number of input mechanisms, such as amicrophone for speech, a touch-sensitive screen for gesture or graphicalinput, keyboard, mouse, motion input, speech and so forth. An outputdevice 235 can also be one or more of a number of output mechanismsknown to those of skill in the art. In some instances, multimodalsystems can enable a user to provide multiple types of input tocommunicate with the computing device 200. The communications interface240 can generally govern and manage the user input and system output.There is no restriction on operating on any particular hardwarearrangement and therefore the basic features here may easily besubstituted for improved hardware or firmware arrangements as they aredeveloped.

Storage device 230 is a non-volatile memory and can be a hard disk orother types of computer readable media which can store data that areaccessible by a computer, such as magnetic cassettes, flash memorycards, solid state memory devices, digital versatile disks, cartridges,random access memories (RAMs) 225, read only memory (ROM) 220, andhybrids thereof.

The storage device 230 can include software modules 232, 234, 236 forcontrolling the processor 210. Other hardware or software modules arecontemplated. The storage device 230 can be connected to the system bus205. In one aspect, a hardware module that performs a particularfunction can include the software component stored in acomputer-readable medium in connection with the necessary hardwarecomponents, such as the processor 210, bus 205, display 235, and soforth, to carry out the function.

FIG. 2B illustrates a computer system 250 having a chipset architecturethat can be used in executing the described method and generating anddisplaying a graphical user interface (GUI). Computer system 250 is anexample of computer hardware, software, and firmware that can be used toimplement the disclosed technology. System 250 can include a processor255, representative of any number of physically and/or logicallydistinct resources capable of executing software, firmware, and hardwareconfigured to perform identified computations. Processor 255 cancommunicate with a chipset 260 that can control input to and output fromprocessor 255. In this example, chipset 260 outputs information tooutput 265, such as a display, and can read and write information tostorage device 270, which can include magnetic media, and solid statemedia, for example. Chipset 260 can also read data from and write datato RAM 275. A bridge 280 for interfacing with a variety of userinterface components 285 can be provided for interfacing with chipset260. Such user interface components 285 can include a keyboard, amicrophone, touch detection and processing circuitry, a pointing device,such as a mouse, and so on. In general, inputs to system 250 can comefrom any of a variety of sources, machine generated and/or humangenerated.

Chipset 260 can also interface with one or more communication interfaces290 that can have different physical interfaces. Such communicationinterfaces can include interfaces for wired and wireless local areanetworks, for broadband wireless networks, as well as personal areanetworks. Some applications of the methods for generating, displaying,and using the GUI disclosed herein can include receiving ordereddatasets over the physical interface or be generated by the machineitself by processor 255 analyzing data stored in storage 270 or 275.Further, the machine can receive inputs from a user via user interfacecomponents 285 and execute appropriate functions, such as browsingfunctions by interpreting these inputs using processor 255.

It can be appreciated that exemplary systems 200 and 250 can have morethan one processor 210 or be part of a group or cluster of computingdevices networked together to provide greater processing capability.

FIG. 3 illustrates a schematic block diagram of an example architecture300 for a network fabric 312. The network fabric 312 can include spineswitches 302 _(A), 302 _(B), . . . , 302 _(N) (collectively “302”)connected to leaf switches 304 _(A), 304 _(B), 304 _(C) . . . 304 _(N)(collectively “304”) in the network fabric 312.

Spine switches 302 can be L3 switches in the fabric 312. However, insome cases, the spine switches 302 can also, or otherwise, perform L2functionalities. Further, the spine switches 302 can support variouscapabilities, such as 40 or 10 Gbps Ethernet speeds. To this end, thespine switches 302 can include one or more 40 Gigabit Ethernet ports.Each port can also be split to support other speeds. For example, a 40Gigabit Ethernet port can be split into four 10 Gigabit Ethernet ports.

In some embodiments, one or more of the spine switches 302 can beconfigured to host a proxy function that performs a lookup of theendpoint address identifier to locator mapping in a mapping database onbehalf of leaf switches 304 that do not have such mapping. The proxyfunction can do this by parsing through the packet to the encapsulatedtenant packet to get to the destination locator address of the tenant.The spine switches 302 can then perform a lookup of their local mappingdatabase to determine the correct locator address of the packet andforward the packet to the locator address without changing certainfields in the header of the packet.

When a packet is received at a spine switch 302 _(i), the spine switch302 _(i) can first check if the destination locator address is a proxyaddress. If so, the spine switch 302 _(i) can perform the proxy functionas previously mentioned. If not, the spine switch 302 _(i) can look upthe locator in its forwarding table and forward the packet accordingly.

Spine switches 302 connect to leaf switches 304 in the fabric 312. Leafswitches 304 can include access ports (or non-fabric ports) and fabricports. Fabric ports can provide uplinks to the spine switches 302, whileaccess ports can provide connectivity for devices, hosts, endpoints,VMs, or external networks to the fabric 312.

Leaf switches 304 can reside at the edge of the fabric 312, and can thusrepresent the physical network edge. In some cases, the leaf switches304 can be top-of-rack (“ToR”) switches configured according to a ToRarchitecture. In other cases, the leaf switches 304 can be aggregationswitches in any particular topology, such as end-of-row (EoR) ormiddle-of-row (MoR) topologies. The leaf switches 304 can also representaggregation switches, for example.

The leaf switches 304 can be responsible for routing and/or bridging thetenant packets and applying network policies. In some cases, a leafswitch can perform one or more additional functions, such asimplementing a mapping cache, sending packets to the proxy function whenthere is a miss in the cache, encapsulate packets, enforce ingress oregress policies, etc.

Moreover, the leaf switches 304 can contain virtual switchingfunctionalities, such as a virtual tunnel endpoint (VTEP) function asexplained below in the discussion of VTEP 408 in FIG. 4. To this end,leaf switches 304 can connect the fabric 312 to an overlay network, suchas overlay network 400 illustrated in FIG. 4.

Network connectivity in the fabric 312 can flow through the leafswitches 304. Here, the leaf switches 304 can provide servers,resources, endpoints, external networks, or VMs access to the fabric312, and can connect the leaf switches 304 to each other. In some cases,the leaf switches 304 can connect EPGs to the fabric 312 and/or anyexternal networks. Each EPG can connect to the fabric 312 via one of theleaf switches 304, for example.

Endpoints 310A-E (collectively “310”) can connect to the fabric 312 vialeaf switches 304. For example, endpoints 310A and 310B can connectdirectly to leaf switch 304A, which can connect endpoints 310A and 310Bto the fabric 312 and/or any other one of the leaf switches 304.Similarly, endpoint 310E can connect directly to leaf switch 304C, whichcan connect endpoint 310E to the fabric 312 and/or any other of the leafswitches 304. On the other hand, endpoints 310C and 310D can connect toleaf switch 304B via L2 network 306. Similarly, the wide area network(WAN) can connect to the leaf switches 304C or 304D via L3 network 308.

Endpoints 310 can include any communication device, such as a computer,a server, a switch, a router, etc. In some cases, the endpoints 310 caninclude a server, hypervisor, or switch configured with a VTEPfunctionality which connects an overlay network, such as overlay network400 below, with the fabric 312. For example, in some cases, theendpoints 310 can represent one or more of the VTEPs 408A-D illustratedin FIG. 4. Here, the VTEPs 408A-D can connect to the fabric 312 via theleaf switches 304. The overlay network can host physical devices, suchas servers, applications, EPGs, virtual segments, virtual workloads,etc. In addition, the endpoints 310 can host virtual workload(s),clusters, and applications or services, which can connect with thefabric 312 or any other device or network, including an externalnetwork. For example, one or more endpoints 310 can host, or connect to,a cluster of load balancers or an EPG of various applications.

Although the fabric 312 is illustrated and described herein as anexample leaf-spine architecture, one of ordinary skill in the art willreadily recognize that the subject technology can be implemented basedon any network fabric, including any data center or cloud networkfabric. Indeed, other architectures, designs, infrastructures, andvariations are contemplated herein.

FIG. 4 illustrates an exemplary overlay network 400. Overlay network 400uses an overlay protocol, such as VXLAN, VGRE, VO3, or STT, toencapsulate traffic in L2 and/or L3 packets which can cross overlay L3boundaries in the network. As illustrated in FIG. 4, overlay network 400can include hosts 406A-D interconnected via network 402.

Network 402 can include a packet network, such as an IP network, forexample. Moreover, network 402 can connect the overlay network 400 withthe fabric 312 in FIG. 3. For example, VTEPs 408A-D can connect with theleaf switches 304 in the fabric 312 via network 402.

Hosts 406A-D include virtual tunnel end points (VTEP) 408A-D, which canbe virtual nodes or switches configured to encapsulate andde-encapsulate data traffic according to a specific overlay protocol ofthe network 400, for the various virtual network identifiers (VNIDs)410A-I. Moreover, hosts 406A-D can include servers containing a VTEPfunctionality, hypervisors, and physical switches, such as L3 switches,configured with a VTEP functionality. For example, hosts 406A and 406Bcan be physical switches configured to run VTEPs 408A-B. Here, hosts406A and 406B can be connected to servers 404A-D, which, in some cases,can include virtual workloads through VMs loaded on the servers, forexample.

In some embodiments, network 400 can be a VXLAN network, and VTEPs408A-D can be VXLAN tunnel end points (VTEP). However, as one ofordinary skill in the art will readily recognize, network 400 canrepresent any type of overlay or software-defined network, such asNVGRE, STT, or even overlay technologies yet to be invented.

The VNIDs can represent the segregated virtual networks in overlaynetwork 400. Each of the overlay tunnels (VTEPs 408A-D) can include oneor more VNIDs. For example, VTEP 408A can include VNIDs 1 and 2, VTEP408B can include VNIDs 1 and 2, VTEP 408C can include VNIDs 1 and 2, andVTEP 408D can include VNIDs 1-3. As one of ordinary skill in the artwill readily recognize, any particular VTEP can, in other embodiments,have numerous VNIDs, including more than the 3 VNIDs illustrated in FIG.4.

The traffic in overlay network 400 can be segregated logically accordingto specific VNIDs. This way, traffic intended for VNID 1 can be accessedby devices residing in VNID 1, while other devices residing in otherVNIDs (e.g., VNIDs 2 and 3) can be prevented from accessing suchtraffic. In other words, devices or endpoints connected to specificVNIDs can communicate with other devices or endpoints connected to thesame specific VNIDs, while traffic from separate VNIDs can be isolatedto prevent devices or endpoints in other specific VNIDs from accessingtraffic in different VNIDs.

Servers 404A-D and VMs 404E-I can connect to their respective VNID orvirtual segment, and communicate with other servers or VMs residing inthe same VNID or virtual segment. For example, server 404A cancommunicate with server 404C and VMs 404E and 404G because they allreside in the same VNID, viz., VNID 1. Similarly, server 404B cancommunicate with VMs 404F and 404H because they all reside in VNID 2.VMs 404E-I can host virtual workloads, which can include applicationworkloads, resources, and services, for example. However, in some cases,servers 404A-D can similarly host virtual workloads through VMs hostedon the servers 404A-D. Moreover, each of the servers 404A-D and VMs404E-I can represent a single server or VM, but can also representmultiple servers or VMs, such as a cluster of servers or VMs.

VTEPs 408A-D can encapsulate packets directed at the various VNIDs 1-3in the overlay network 400 according to the specific overlay protocolimplemented, such as VXLAN, so traffic can be properly transmitted tothe correct VNID and recipient(s). Moreover, when a switch, router, orother network device receives a packet to be transmitted to a recipientin the overlay network 400, it can analyze a routing table, such as alookup table, to determine where such packet needs to be transmitted sothe traffic reaches the appropriate recipient. For example, if VTEP 408Areceives a packet from endpoint 404B that is intended for endpoint 404H,VTEP 408A can analyze a routing table that maps the intended endpoint,endpoint 404H, to a specific switch that is configured to handlecommunications intended for endpoint 404H. VTEP 408A might not initiallyknow, when it receives the packet from endpoint 404B, that such packetshould be transmitted to VTEP 408D in order to reach endpoint 404H.Accordingly, by analyzing the routing table, VTEP 408A can lookupendpoint 404H, which is the intended recipient, and determine that thepacket should be transmitted to VTEP 408D, as specified in the routingtable based on endpoint-to-switch mappings or bindings, so the packetcan be transmitted to, and received by, endpoint 404H as expected.

However, continuing with the previous example, in many instances, VTEP408A may analyze the routing table and fail to find any bindings ormappings associated with the intended recipient, e.g., endpoint 404H.Here, the routing table may not yet have learned routing informationregarding endpoint 404H. In this scenario, the VTEP 408A may likelybroadcast or multicast the packet to ensure the proper switch associatedwith endpoint 404H can receive the packet and further route it toendpoint 404H.

In some cases, the routing table can be dynamically and continuouslymodified by removing unnecessary or stale entries and adding new ornecessary entries, in order to maintain the routing table up-to-date,accurate, and efficient, while reducing or limiting the size of thetable.

As one of ordinary skill in the art will readily recognize, the examplesand technologies provided above are simply for clarity and explanationpurposes, and can include many additional concepts and variations.

Depending on the desired implementation in the network 400, a variety ofnetworking and messaging protocols may be used, including but notlimited to TCP/IP, open systems interconnection (OSI), file transferprotocol (FTP), universal plug and play (UpnP), network file system(NFS), common internet file system (CIFS), AppleTalk etc. As would beappreciated by those skilled in the art, the network 400 illustrated inFIG. 4 is used for purposes of explanation, a network system may beimplemented with many variations, as appropriate, in the configurationof network platform in accordance with various embodiments of thepresent disclosure.

Having disclosed a brief introductory description of exemplary systemsand networks, the discussion now turns to adaptive telemetry based onin-network cross domain intelligence. A telemetry server can receivetelemetry data streams from multiple sensors in a network. Eachtelemetry data stream can include data collected from a data source,such as data from various compute and infrastructure domains, includingapplication, platform (e.g., operations system) infrastructure (e.g.,network) and physical (e.g., power/temperature). The telemetry servercan analyze the telemetry data streams to determine correlations betweenthe telemetry data streams that indicate redundancies in data. Forexample, the telemetry server can utilize multi-modal deep learningtechniques to create models of the telemetry data streams and predictredundant data in the telemetry data streams. The telemetry server canthen adjust data collection of the telemetry data streams based on thecorrelations to reduce collection of redundant data.

FIG. 5 illustrates a system for adaptive telemetry based on in-networkcross domain intelligence. As shown, system 500 includes telemetryserver 502 that can receive telemetry data streams from sensors 504 and506. Sensors 504 and 506 can each include one or more sensors and/orcomputing devices configured to capture data from a data source andapply sampling and compression to the captured data to create atelemetry data stream. Sensors 504 and 506 can then transmit thetelemetry data stream to telemetry server 502. As shown, sensor 504gathers data from data source 508 and sensor 506 gathers data from datasource 510. Data sources 508 and 510 can be any grouping of computingdevices, applications, VMs, switches, etc. Sensors 504 and 506 cancapture diverse data from various layers or compute and infrastructuredomains, including application, platform (e.g., OS), infrastructure(e.g., network) and physical (e.g., power, temperature, etc.).

Sensors 504 and 506 can apply sampling and compression techniques toreduce the amount of data included in a telemetry data streamtransmitted to telemetry server 502. Sampling can include varying therate at which data is captured and/or included in a telemetry datastream. Compression can include applying compression algorithms thatanalyze the raw data and include only summarized results of the data inthe telemetry data stream, while still preserving the context of thedata.

Telemetry server 502 can forward the received telemetry data stream toone or more computing devices, such as computing devices 512 and 514,where the data can be reconstructed, stored and analyzed. To reduce theamount of data that is included in the telemetry data stream, telemetryserver 502 can be configured to analyze the telemetry data streams toidentify correlations between the data streams that indicateredundancies in the data included in the telemetry data streams. Forexample, telemetry server 502 can utilize multi-modal deep learning toidentify correlation between the data streams.

Telemetry server 502 can use multi-modal deep learning to create modelsof the telemetry data signals, which can be used to learn how thetelemetry data signals are correlated. Telemetry server 502 can usethese models and correlations to predict future performance of atelemetry data signal, such as what data will be included in thetelemetry data signal during specified periods of time.

In some embodiments, telemetry server 502 can utilize multimodal deeplearning to identify telemetry signals in the data that indicate dataredundancies for a specified time period. Likewise, telemetry server 502can utilize multimodal deep learning to identify specified periods oftime when telemetry data streams will include unique data (i.e., notredundant data). For example, telemetry server 502 can create models ofmultiple telemetry data signals and compare the models to identifyredundant data and/or unique data.

Based on the models, telemetry server 502 can identify one or moretelemetry signals such as a time of day, week, month, etc., during whichtwo or more telemetry data streams will or will not include redundantdata for a specified time period. As another example, telemetry server502 can determine a telemetry signal such as an occurrence of data orcombination of data in a telemetry data signal that indicates that twoor more telemetry data streams will or will not include redundant datafor a specified time period.

Telemetry server 502 can be configured to adjust data collection of thetelemetry data streams based on the correlations. For example telemetryserver 502 can monitor the telemetry data streams for the identifiedtelemetry signals and, upon detection of a telemetry signal, transmitcommands to sensor 504 and/or sensor 506 to adjust sampling and/orcompression of their respective telemetry data stream to reduceredundant data. Likewise, telemetry server 502 can transmit commands tocomputing devices 512 and 514 to adjust reconstruction of telemetry datastreams to reduce redundant data.

As an example, telemetry server 502 can monitor data streams receivedfrom sensors 504 and 506 including data captured from data sources 508and 501. In response to detecting a telemetry signal indicating thatdata included in the telemetry data streams will be redundant at aspecified time or during a specified time period, telemetry server 502can transmit a command to sensor 504 and/or sensor 506 to reduce therate at which data is sampled from data source 508 and/or data source510 for inclusion in the telemetry data stream. As another example, inresponse to detecting a telemetry signal indicating that data includedin the telemetry data streams will be unique, for example as a result ofthe specified time period having lapsed, telemetry server 502 cantransmit a command to sensor 504 and/or 506 to readjust the rate atwhich data is sampled from data source 508 and/or 510 to a previousstate.

Telemetry server 502 can continuously apply multimodal deep learningtechniques to the telemetry data streams to identify correlationsbetween the telemetry data stream and adjust data collection to reduceredundant data in the telemetry data streams. In this way, telemetryserver 502 can continue to learn from the telemetry data streams andadjust data collection to reduce data redundancies. Telemetry server 502can also use other known data reduction strategies, in addition tomultimodal deep learning, to reduce redundant data collection.

To ensure that the created models are accurate, telemetry server 502 cantest the created models. For example, telemetry server 502 can removeone of the inputs and rest the ability of the shared model to predictthe missing modality.

FIG. 6 illustrates an example method of adaptive telemetry based onin-network cross domain intelligence. It should be understood that therecan be additional, fewer, or alternative steps performed in similar oralternative orders, or in parallel, within the scope of the variousembodiments unless otherwise stated.

At step 602 a telemetry server can receive at least a first telemetrydata stream and a second telemetry data stream. The first telemetry datastream can provide data collected from a first data source and thesecond telemetry data stream can provide data collected from a seconddata source. The data sources can include various types of data, such asapplication, platform (e.g., operations system) infrastructure (e.g.,network) and physical (e.g., power/temperature).

At step 604, the telemetry server can determine correlations between thefirst telemetry data stream and the second telemetry data stream thatindicate redundancies between data included in the first telemetry datastream and the second telemetry data stream. For example, the telemetryserver can utilize multimodal deep learning techniques to identifytelemetry signals indicating that the second telemetry data stream willinclude redundant data for a specified time period following anoccurrence of the telemetry signal.

At step 606, the telemetry server can adjust, based on the correlationsbetween the first telemetry data stream and the second telemetry datastream, data collection of the second telemetry data stream to reduceredundant data included in the first telemetry data stream and thesecond telemetry data stream. For example the telemetry server canmonitor the telemetry data streams for a telemetry signal indicatingthat the second telemetry data stream will include redundant data andadjust data collection of the second telemetry data stream in responseto detecting the telemetry signal. This can include reducing a samplingrate at which data is collected from the second data source, adjusting acompression method applied to data collected from the second datasource, reducing a reconstruction rate for data included in the secondtelemetry data stream, etc., for the specified time period. After thespecified time period has lapsed, the telemetry server can readjustingdata collection of the second data stream to a previous state, such asthe state prior to detecting the telemetry signal.

As one of ordinary skill in the art will readily recognize, the examplesand technologies provided above are simply for clarity and explanationpurposes, and can include many additional concepts and variations.

For clarity of explanation, in some instances the present technology maybe presented as including individual functional blocks includingfunctional blocks comprising devices, device components, steps orroutines in a method embodied in software, or combinations of hardwareand software.

In some embodiments the computer-readable storage devices, mediums, andmemories can include a cable or wireless signal containing a bit streamand the like. However, when mentioned, non-transitory computer-readablestorage media expressly exclude media such as energy, carrier signals,electromagnetic waves, and signals per se.

Methods according to the above-described examples can be implementedusing computer-executable instructions that are stored or otherwiseavailable from computer readable media. Such instructions can comprise,for example, instructions and data which cause or otherwise configure ageneral purpose computer, special purpose computer, or special purposeprocessing device to perform a certain function or group of functions.Portions of computer resources used can be accessible over a network.The computer executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, firmware, orsource code. Examples of computer-readable media that may be used tostore instructions, information used, and/or information created duringmethods according to described examples include magnetic or opticaldisks, flash memory, USB devices provided with non-volatile memory,networked storage devices, and so on.

Devices implementing methods according to these disclosures can comprisehardware, firmware and/or software, and can take any of a variety ofform factors. Typical examples of such form factors include laptops,smart phones, small form factor personal computers, personal digitalassistants, rackmount devices, standalone devices, and so on.Functionality described herein also can be embodied in peripherals oradd-in cards. Such functionality can also be implemented on a circuitboard among different chips or different processes executing in a singledevice, by way of further example.

The instructions, media for conveying such instructions, computingresources for executing them, and other structures for supporting suchcomputing resources are means for providing the functions described inthese disclosures.

Although a variety of examples and other information was used to explainaspects within the scope of the appended claims, no limitation of theclaims should be implied based on particular features or arrangements insuch examples, as one of ordinary skill would be able to use theseexamples to derive a wide variety of implementations. Further andalthough some subject matter may have been described in languagespecific to examples of structural features and/or method steps, it isto be understood that the subject matter defined in the appended claimsis not necessarily limited to these described features or acts. Forexample, such functionality can be distributed differently or performedin components other than those identified herein. Rather, the describedfeatures and steps are disclosed as examples of components of systemsand methods within the scope of the appended claims. Moreover, claimlanguage reciting “at least one of” a set indicates that one member ofthe set or multiple members of the set satisfy the claim.

1. A method comprising: identifying, by a telemetry server, a telemetrysignal indicating that a telemetry data stream will include redundantdata for a time period following an occurrence of the telemetry signal;and adjusting, upon detection of the telemetry signal, data collectionof the telemetry data stream to reduce redundant data included in thetelemetry data stream and another telemetry data stream.
 2. The methodof claim 1, wherein adjusting data collection of the telemetry datastream comprises: reducing a sampling rate at which data is collectedfrom a data source for inclusion in the telemetry data stream.
 3. Themethod of claim 1, wherein adjusting data collection of the telemetrydata stream comprises: adjusting a compression method applied to datacollected from a data source for inclusion in the telemetry data stream.4. The method of claim 1, wherein adjusting data collection of thetelemetry data stream comprises: reducing a reconstruction rate for dataincluded in the telemetry data stream.
 5. The method of claim 1, furthercomprising: receive the telemetry data stream and the another telemetrydata stream, the telemetry data stream providing data collected from adata source and the another telemetry data stream providing datacollected from another data source.
 6. The method of claim 1, furthercomprising: after adjusting data collection of the telemetry datastream: determining that the time period following occurrence of thetelemetry signal has lapsed, and readjusting data collection of a datastream to a previous state.
 7. The method of claim 1, furthercomprising: determining correlations between the telemetry data streamand the another telemetry data stream indicating redundancies betweendata included in the telemetry data stream and the another telemetrydata stream using multimodal deep learning.
 8. A system comprising: oneor more computer processors; and a memory storing instructions that,when executed by the one or more computer processors, cause the systemto: identify a telemetry signal indicating that a telemetry data streamwill include redundant data for a time period following an occurrence ofthe telemetry signal; and adjusting, upon detection of the telemetrysignal, data collection of the telemetry data stream to reduce redundantdata included in the telemetry data stream and another telemetry datastream.
 9. The system of claim 8, wherein adjusting data collection ofthe telemetry data stream comprises: reducing a sampling rate at whichdata is collected from a data source for inclusion in the telemetry datastream.
 10. The system of claim 8, wherein adjusting data collection ofthe telemetry data stream comprises: adjusting a compression methodapplied to data collected from a data source for inclusion in thetelemetry data stream.
 11. The system of claim 8, wherein adjusting datacollection of the telemetry data stream comprises: reducing areconstruction rate for data included in the telemetry data stream. 12.The system of claim 8, wherein the instructions further cause the systemto: receive the telemetry data stream and the another telemetry datastream, the telemetry data stream providing data collected from a datasource and the another telemetry data stream providing data collectedfrom another data source.
 13. The system, of claim 8, wherein theinstructions further cause the system to: after adjusting datacollection of the telemetry data stream: determine that the time periodfollowing occurrence of the telemetry signal has lapsed, and readjustdata collection of a data stream to a previous state.
 14. The system ofclaim 8, further comprising: determining correlations between thetelemetry data stream and the another telemetry data stream indicatingredundancies between data included in the telemetry data stream and theanother telemetry data stream using multimodal deep learning.
 15. Anon-transitory computer-readable medium storing instructions that, whenexecuted by the one or more computer processors of a computing device,cause the computing device to: identify a telemetry signal indicatingthat a telemetry data stream will include redundant data for a timeperiod following an occurrence of the telemetry signal; and adjust, upondetection of the telemetry signal, data collection of the telemetry datastream to reduce redundant data included in the telemetry data streamand another telemetry data stream.
 16. The non-transitorycomputer-readable medium of claim 15, wherein adjusting data collectionof the telemetry data stream comprises: reducing a sampling rate atwhich data is collected from a data source for inclusion in thetelemetry data stream.
 17. The non-transitory computer-readable mediumof claim 15, wherein adjusting data collection of the telemetry datastream comprises: adjusting a compression method applied to datacollected from a data source for inclusion in the telemetry data stream.18. The non-transitory computer-readable medium of claim 15, whereinadjusting data collection of the telemetry data stream comprises:reducing a reconstruction rate for data included in the telemetry datastream.
 19. The non-transitory computer-readable medium, of claim 15,wherein the instructions further cause the computing device to: receivethe telemetry data stream and the another telemetry data stream, thetelemetry data stream providing data collected from a data source andthe another telemetry data stream providing data collected from anotherdata source.
 20. The non-transitory computer-readable medium, of claim15, wherein the instructions further cause the computing device to:after adjusting data collection of the telemetry data stream: determinethat the time period following occurrence of the telemetry signal haslapsed, and readjust data collection of a data stream to a previousstate.